title: IDBA-tran: a more robust de novo de Bruijn graph assembler for transcriptomes with uneven expression levels.
abstract: BACKGROUND
  RNA sequencing based on next-generation sequencing technology is effective for analyzing transcriptomes. Like de novo genome assembly, de novo transcriptome assembly does not rely on any reference genome or additional annotation information, but is more difficult. In particular, isoforms can have very uneven expression levels (e.g. 1:100), which make it very difficult to identify low-expressed isoforms. One challenge is to remove erroneous vertices/edges with high multiplicity (produced by high-expressed isoforms) in the de Bruijn graph without removing correct ones with not-so-high multiplicity from low-expressed isoforms. Failing to do so will result in the loss of low-expressed isoforms or having complicated subgraphs with transcripts of different genes mixed together due to erroneous vertices/edges. Contributions: Unlike existing tools, which remove erroneous vertices/edges with multiplicities lower than a global threshold, we use a probabilistic progressive approach to iteratively remove them with local thresholds. This enables us to decompose the graph into disconnected components, each containing a few genes, if not a single gene, while retaining many correct vertices/edges of low-expressed isoforms. Combined with existing techniques, IDBA-Tran is able to assemble both high-expressed and low-expressed transcripts and outperform existing assemblers in terms of sensitivity and specificity for both simulated and real data.
  
  
  BACKGROUND
  http://www.cs.hku.hk/~alse/idba_tran.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, The University of Hong Kong, Hong Kong, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Peng
authors.forename: Henry C M
authors.initials: HC
authors.lastname: Leung
authors.forename: Siu-Ming
authors.initials: SM
authors.lastname: Yiu
authors.forename: Ming-Ju
authors.initials: MJ
authors.lastname: Lv
authors.forename: Xin-Guang
authors.initials: XG
authors.lastname: Zhu
authors.forename: Francis Y L
authors.initials: FY
authors.lastname: Chin
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D020869:Gene Expression Profiling / Q000379:methods*; D016678:Genome; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D012275:Oryza / Q000235:genetics / Q000378:metabolism; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software